1. Field of the Invention
This invention relates to a rich expression of data used in supply chain management, multi-criteria ranking, real-time auctions and risk assessment.
2. Discussion of Prior Art
Prasanna et al [1] applies linear constraints to traffic problems in telecommunication. It does not discuss information content, and does not contain any reference to Supply Chains. Stochastic Programming Shapiro et al [2], Shabbir Ahmed et al [3] and robust programming Bertsimas and Sim [7] are two classical techniques for handling uncertainty in algorithms, based respectively on minimizing the expected value of a metric, and/or a worst case value (or a weighted combination of the two). In SP, a probabilistic formulation of the world is used, and single/dual stage optimization (with recourse) can be used to optimize expected and/or K'th percentile (e.g. 90th percentile) values of the size, capacity, cost, etc. The results are dependent on the probability distribution assumed, which is difficult to estimate in practice. As opposed to this, robust programming assumes a set of scenarios, and optimizes the worst case value of the metric over the set of scenarios. Even in RP, generating the set of scenarios is a difficult task. The main focus of Shapiro and Nemirovski [4] is again on the use of probabilistic distributions and their problems and the resulting complexity. Recent work on Robust optimization can be found in [5], [6], [7], [8], but neither applies linear constraints to model detailed economic behavior, nor quantifies information content. The methods developed by Bertsimas and Sim [7], by imposing a controllable amount of uncertainty in the input data, do not treat all the kinds of uncertainty we deal with here.
Our work has shown the capabilities of linear constraints to incorporate meaningful economic behavior (substitutive/complimentary behavior) and given a quantitative information theoretic interpretation. Our linear constraints are able to incorporate much richer economic information compared to [7] and [8]. Gan et al [9] only deals with complexity of supply chain based on probability distributions of various parameters and does not do design and optimization, and also does not have hierarchical constraints. The present invention incorporates the ideas of hierarchical constraints and does both design and optimization. Recent work on reverse auctions [10] does not incorporate uncertainty in benefits, which our model deals
U.S. Pat. No. 758,509 deals with using customer forecasted demands to forecast the direct material to be used for production of products in accordance with the customer forecasted demands by a supply chain server. But our model does not use demand forecasts. We rather use a hierarchy of linear constraints to determine the optimal demand of material required to meet the demand of the products in the end market. Our model also takes into consideration the uncertainty in the demand of products. U.S. Pat. No. 191,910, U.S. Pat. No. 155,175, U.S. Pat. No. 735,634, U.S. Pat. No. 611,253 and other patents also talk about using forecasting and predicting the values of demand but they essentially do not use our information theoretic constraint based approach.